This invention relates to a method and system for receiving repair recommendations and related information from a central diagnostic and repair service center at a remote location, for repairing, for instance, a railroad locomotive.
The diagnosis, maintenance, and repair of a complex vehicle, such as an off-road vehicle, ship, airplane or railroad locomotive involves extremely complex and time consuming processes. Efficient and cost-effective operation of a fleet of such vehicles necessitates a reduction in the number of vehicle road failures and minimization of vehicle down-time. This can be accomplished by predicting impending failures, by performing preventative maintenance, and by performing repairs quickly and accurately. For example, it will be appreciated that the ability to predict failures before they occur allows for performing condition-based maintenance. Such maintenance can be conveniently scheduled based on statistically and probabilistically meaningful vehicle status information, instead of performing the maintenance regardless of the actual condition of the subsystem, such as would be the case if the maintenance is routinely performed independent of whether the subsystem actually requires the maintenance.
The conventional diagnosis and repair process for most vehicles and machines is based on the experience of the service technician, using paper-based information describing the structure and operation of the machine, and performance records maintained in a log. Examining the log entries, experienced service technicians can use their accumulated experience and training in mapping incidents occurring in locomotive subsystems to problems that may be causing these incidents. For simple problems, this process works well. However, if the problem is complex and the root cause difficult to discern, the experienced technician may be unable to identify the problem and certainly much less likely to prognosticate problems before they occur.
A machine, such as a locomotive or other mobile asset used in industrial processes, telecommunications, aerospace applications, power generation, etc. often incorporates diagnostic controls and sensors that report faults when anomalous operating conditions of the machine arise. Typically, to diagnose the problem, a technician will study the fault log to identify the nature of the problem and determine whether a repair is necessary. While the fault log can provide some diagnosis and repair information, the technician also relies substantially on his prior experiences with the machine, or others like it, to make a full diagnosis.
To conduct the repair, the technician uses block diagrams, exploded diagrams, parts lists, assembly drawings, schematics, etc. The repair information may be applicable only to a specific machine by model number; the repair information will generally not be unique to the specific machine undergoing repair. It is obvious that as the complexity of the machine increases, the amount of paper needed to describe the machine to assist with the repair process likewise increases. Again, the technician will rely on his experiences with the machine, and others like it, to perform the repair.
Yet another problem with a paper-based system is the variety of fielded configurations, each having its own unique technical support documentation. Even for a specific model (identified by a model number), there may be several locomotive configurations as locomotive subsystems were redesigned or changed during the model production run. Thus, in a sense, no two locomotives are the same. Adding this configuration complexity to a paper-based system presents an inordinately complex and unmanageable problem of locating the correct technical repair documentation for a specific locomotive.
Another repair issue involving complex machines, such as railroad locomotives or other mobile assets, is the unavailability of a repair history from which one could predict component failures and undertake preventative maintenance beforehand. Technicians with wide ranging and lengthy experiences may be able to predict a component failure and repair it to avoid a breakdown during operation, in some limited situations.
One tool available for locomotive repair manually downloads fault logs from a locomotive while it is parked at a maintenance facility. These fault logs are then uploaded to the railroad maintenance service center. The tool also includes standardized helpful hints for repair tasks and fault analysis descriptors based on single failure faults. Although such a device represents an improvement over a paper-based system, it falls short of the informational needs for a complex machine, such as a locomotive, and fails to advantageously utilize the various technologies available for more efficiently predicting and performing the repair.